Deconvolution of clinical variance in CAR-T cell pharmacology and response

Menée à partir de données cliniques portant sur des patients atteints d'une leucémie lymphoblastique aiguë, d'un lymphome à grandes cellules B ou d'un lymphome lymphocytique chronique, cette étude met en évidence l'intérêt de modèles mathématiques pour décrire les réponses antitumorales des lymphocytes T, identifier des facteurs influençant les résultats cliniques et découvrir des signatures biologiques prédictives

Résumé en anglais

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.